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Found 3 papers, 1 papers with code
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems
Employing a high-efficiency Graph Neural Network (GNN) for global dividing and a fixed-length sub-path solver for conquering sub-problems, the proposed UDC framework demonstrates extensive applicability, achieving superior performance in 10 representative large-scale CO problems.
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Combinatorial Optimization
The neural combinatorial optimization (NCO) approach has shown great potential for solving routing problems without the requirement of expert knowledge.
Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed
To the best of our knowledge, this study is first-of-its-kind to obtain NAR VRP solvers from AR ones through knowledge distillation.
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